system

Abstract

The system according to the embodiment aims to automatically manage ingredients in a refrigerator, and to provide efficient inventory management and recipe suggestions. [Solution] The system according to the embodiment includes a camera installation unit, an analysis unit, a transfer unit, an ordering unit, and a suggestion unit. The camera installation unit installs a camera to visualize ingredients in the refrigerator. The analysis unit analyzes the images acquired by the camera installation unit. The transfer unit transfers the data analyzed by the analysis unit to a smartphone. The order unit manages the inventory of ingredients based on the data transferred by the transfer unit, and places an online order when the inventory falls below a threshold. The suggestion unit suggests specific recipes based on the data acquired by the analysis unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, managing the inventory of ingredients in the refrigerator and suggesting recipes were done manually, which presented challenges in efficient management.

[0005] The system according to the embodiment aims to automatically manage ingredients in a refrigerator, and to provide efficient inventory management and recipe suggestions. [Means for solving the problem]

[0006] The system according to this embodiment comprises a camera installation unit, an analysis unit, a data transfer unit, an order unit, and a suggestion unit. The camera installation unit installs a camera to visualize the food items inside the refrigerator. The analysis unit analyzes the images acquired by the camera installation unit. The data transfer unit transfers the data analyzed by the analysis unit to a smartphone. The order unit manages the amount of food inventory based on the data transferred by the data transfer unit and places an online order when the amount falls below a threshold. The suggestion unit suggests specific recipes based on the data acquired by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment automatically manages ingredients in a refrigerator, enabling efficient inventory management and recipe suggestions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) A system according to an embodiment of the present invention digitizes and efficiently manages ingredients stored in a refrigerator. This system installs a camera inside the refrigerator and visualizes the ingredients using camera images. The captured images are then transferred to a smartphone, where they are loaded and learned from previously created recipes. A threshold is set for the amount of ingredients in stock, and online orders are placed when the amount falls below the threshold. Furthermore, recommended recipes are suggested based on the captured images. For example, a camera is installed inside the refrigerator and visualizes the ingredients using camera images. The camera is positioned so that it can cover the entire interior of the refrigerator, and AI is used to accurately grasp the types of ingredients and the amount in stock. The captured images are then transferred to a smartphone. Data is transferred in real time using Wi-Fi or Bluetooth (registered trademark). This allows users to check the information about ingredients in the refrigerator on their smartphones. Furthermore, previously created recipes are loaded and the AI ​​learns from them. This allows optimal recipes to be suggested based on the current ingredients. A threshold is set for the amount of ingredients in stock, and online orders are placed when the amount falls below the threshold. This allows for automatic replenishment of ingredients. Finally, recommended recipes are suggested based on the captured images. AI is used to learn from past recipe data and suggest optimal recipes based on the current ingredients. This allows the system to efficiently manage ingredients in the refrigerator, automatically replenish stock, and suggest optimal recipes.

[0029] The refrigerator management system according to this embodiment comprises a camera installation unit, an analysis unit, a data transfer unit, an ordering unit, and a suggestion unit. The camera installation unit installs a camera to visualize the food items inside the refrigerator. The camera installation unit installs the camera in a position that can cover the entire interior of the refrigerator. The analysis unit analyzes the images acquired by the camera installation unit. The analysis unit accurately determines the types and quantities of food items using, for example, AI. The data transfer unit transfers the data analyzed by the analysis unit to a smartphone. The data transfer unit transfers the data in real time using, for example, Wi-Fi or Bluetooth. The ordering unit manages the food inventory based on the data transferred by the transfer unit and places an online order when the inventory falls below a threshold. The ordering unit sets a threshold for the food inventory and places an online order when the inventory falls below the threshold. The suggestion unit proposes specific recipes based on the data acquired by the analysis unit. The suggestion unit learns from past recipe data and proposes an appropriate recipe based on the current ingredients. As a result, the refrigerator management system according to this embodiment can efficiently manage the ingredients in the refrigerator, automatically replenish inventory when it runs low, and propose the optimal recipe.

[0030] The camera unit can be installed in a position that can cover the entire interior of the refrigerator. For example, the camera unit can be installed in a position that can cover the entire interior of the refrigerator. The camera unit can be installed in a position that can cover the entire interior of the refrigerator. This allows for accurate identification of food types and quantities by covering the entire interior of the refrigerator. Some or all of the above-described processes in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can cover the entire interior of the refrigerator by installing the camera in a position that can cover the entire interior of the refrigerator and adjusting the camera's field of view.

[0031] The analysis unit can accurately grasp the type and inventory of ingredients. The analysis unit, for example, uses AI to accurately grasp the type and inventory of ingredients. The analysis unit, for example, uses AI to accurately grasp the type and inventory of ingredients. The analysis unit, for example, uses AI to accurately grasp the type and inventory of ingredients. This allows for accurate understanding of the type and inventory of ingredients, enabling appropriate management. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input images acquired by the camera installation unit into AI, and have the AI ​​analyze the type and inventory of ingredients.

[0032] The transfer unit can transfer data instantly using Wi-Fi or Bluetooth. The transfer unit transfers data in real time using, for example, Wi-Fi or Bluetooth. The transfer unit transfers data instantly using Wi-Fi or Bluetooth. The transfer unit transfers data in real time using Wi-Fi or Bluetooth. By transferring data in real time, the user can instantly check information about ingredients in the refrigerator. Some or all of the above-described processing in the transfer unit may be performed using, for example, AI, or may be performed without using AI. For example, the transfer unit may input data analyzed by the analysis unit into AI and have the AI ​​transfer the data.

[0033] The ordering unit sets a threshold for the inventory of ingredients and can place an online order when the inventory falls below the threshold. The ordering unit, for example, sets a threshold for the inventory of ingredients and can place an online order when the inventory falls below the threshold. The ordering unit sets a threshold for the inventory of ingredients and can place an online order when the inventory falls below the threshold. The ordering unit sets a threshold for the inventory of ingredients and can place an online order when the inventory falls below the threshold. This allows ingredients to be automatically replenished when they are in short supply. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the data transferred by the transfer unit into AI and have the AI ​​manage the inventory of ingredients and place online orders.

[0034] The suggestion unit can learn past recipe data and suggest an appropriate recipe based on the current ingredients. The suggestion unit, for example, learns past recipe data and suggests an appropriate recipe based on the current ingredients. The suggestion unit learns past recipe data and suggests an appropriate recipe based on the current ingredients. The suggestion unit learns past recipe data and suggests an appropriate recipe based on the current ingredients. This makes it possible to suggest an optimal recipe based on the current ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data acquired by the analysis unit into AI and have the AI ​​execute recipe suggestions.

[0035] The camera installation unit can select an appropriate installation location taking into account the temperature and humidity inside the refrigerator. For example, the camera installation unit installs the camera in a location that avoids high temperatures inside the refrigerator to prevent fogging of the lens. The camera installation unit selects an appropriate installation location taking into account the temperature and humidity inside the refrigerator. The camera installation unit can also install the camera in a location that avoids high humidity inside the refrigerator to prevent camera failure. The camera installation unit can also select a location with a good balance between temperature and humidity to ensure the camera's long life. This allows the camera's performance to be maximized by optimizing the camera's installation location. Some or all of the above-mentioned processing in the camera installation unit may be performed using, or without, AI, for example. For example, the camera installation unit can input data on the temperature and humidity inside the refrigerator into AI and have the AI ​​select the optimal installation location.

[0036] The camera mounting unit can automatically recognize the arrangement of shelves and food items inside the refrigerator and determine the optimal mounting position. For example, the camera mounting unit can automatically recognize the arrangement of shelves and mount the camera in a position that does not obstruct the view. The camera mounting unit can automatically recognize the arrangement of shelves and food items inside the refrigerator and determine the optimal mounting position. The camera mounting unit can also automatically recognize the arrangement of food items and mount the camera in a position where important food items are visible. The camera mounting unit can also comprehensively consider the arrangement of shelves and food items and mount the camera in a position that ensures the optimal field of view. By optimizing the camera mounting position in this way, food items can be accurately identified without obstructing the view. Some or all of the above processing in the camera mounting unit may be performed using AI, for example, or without AI. For example, the camera mounting unit can input data on the arrangement of shelves and food items inside the refrigerator into AI and have the AI ​​determine the optimal mounting position.

[0037] The camera mounting unit can select an appropriate mounting position considering the frequency of opening and closing the refrigerator door. For example, the camera mounting unit can install the camera in a location that avoids frequent door opening and closing to prevent interference with filming. The camera mounting unit selects an appropriate mounting position considering the frequency of opening and closing the refrigerator door. The camera mounting unit can also select a location that is not frequently opened and closed to ensure a stable filming environment. The camera mounting unit can also install the camera in a fixed position to avoid vibrations caused by opening and closing the door. In this way, interference with filming can be prevented by optimizing the camera mounting position. Some or all of the above processing in the camera mounting unit may be performed using AI, for example, or without using AI. For example, the camera mounting unit can input data on the frequency of opening and closing the refrigerator door into the AI ​​and have the AI ​​select the optimal mounting position.

[0038] The camera installation unit can select an appropriate installation position taking into account the lighting conditions inside the refrigerator. For example, the camera installation unit installs the camera in a location that avoids direct lighting to prevent overexposure in the image. The camera installation unit selects an appropriate installation position taking into account the lighting conditions inside the refrigerator. The camera installation unit can also select a location where lighting is evenly shining and obtain bright, clear images. The camera installation unit can also avoid locations with poor lighting conditions and install the camera in a location where shadows are less likely to form. In this way, bright, clear images can be obtained by optimizing the camera installation position. Some or all of the above-described processing in the camera installation unit may be performed using, or without, AI. For example, the camera installation unit can input lighting condition data inside the refrigerator into AI and have the AI ​​select the optimal installation position.

[0039] The analysis unit can provide analysis results taking into account the freshness and expiration date of the ingredients. The analysis unit, for example, analyzes the freshness of the ingredients and displays them in order of freshness. The analysis unit provides analysis results taking into account the freshness and expiration date of the ingredients. The analysis unit can also prioritize displaying ingredients with an approaching expiration date to encourage early consumption. The analysis unit can also propose an optimal consumption order by comprehensively considering freshness and expiration date. In this way, the optimal consumption order can be proposed by taking into account the freshness and expiration date of the ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the freshness and expiration date of the ingredients into AI and have the AI ​​provide the analysis results.

[0040] The analysis unit can provide analysis results by adding nutritional value and calorie information of ingredients. For example, the analysis unit can analyze the nutritional value of ingredients and prioritize displaying ingredients with a good nutritional balance. The analysis unit can provide analysis results by adding nutritional value and calorie information of ingredients. The analysis unit can also analyze calorie information and prioritize displaying low-calorie ingredients. The analysis unit can also comprehensively consider nutritional value and calorie information and suggest healthy ingredients. In this way, healthy ingredients can be suggested by considering the nutritional value and calorie information of ingredients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input nutritional value and calorie information data of ingredients into AI and have the AI ​​perform the provision of analysis results.

[0041] The analysis unit can provide analysis results while taking into account the origin information of the ingredients. For example, the analysis unit can analyze the origin information of the ingredients and prioritize the display of locally produced ingredients. The analysis unit can provide analysis results while taking into account the origin information of the ingredients. The analysis unit can also distinguish and display imported ingredients and domestic ingredients based on the origin information. The analysis unit can also comprehensively consider the origin information and suggest ingredients that suit the user's preferences. In this way, by considering the origin information of the ingredients, it is possible to suggest ingredients that suit the user's preferences. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the origin information data of the ingredients into AI and have the AI ​​perform the provision of analysis results.

[0042] The analysis unit can provide analysis results by adding allergen information of food ingredients. For example, the analysis unit can analyze allergen information of food ingredients and display a warning for ingredients containing allergens. The analysis unit can also provide analysis results by adding allergen information of food ingredients. Based on the allergen information, the analysis unit can also prioritize displaying ingredients that do not contain allergens. The analysis unit can also comprehensively consider allergen information and suggest ingredients that are considerate of allergies. In this way, by considering allergen information of food ingredients, ingredients that are considerate of allergies can be suggested. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input allergen information data of food ingredients into AI and have the AI ​​perform the provision of analysis results.

[0043] The transfer unit can select a transfer method considering the smartphone's battery level during data transfer. For example, if the smartphone's battery level is low, the transfer unit will temporarily suspend data transfer. The transfer unit selects a transfer method considering the smartphone's battery level during data transfer. If the battery level is sufficient, the transfer unit can also perform high-speed data transfer using Wi-Fi. If the battery level is moderate, the transfer unit can also perform power-saving data transfer using Bluetooth. This allows for the selection of the optimal transfer method according to the smartphone's battery level, thereby reducing battery consumption. Some or all of the above processing in the transfer unit may be performed using AI, for example, or without AI. For example, the transfer unit can input smartphone battery level data into AI and have the AI ​​select the transfer method.

[0044] The transfer unit can select a transfer method considering the smartphone's network connection status during data transfer. For example, if the network connection is unstable, the transfer unit will temporarily suspend data transfer. The transfer unit selects a transfer method considering the smartphone's network connection status during data transfer. If the network connection is stable, the transfer unit can also perform high-speed data transfer using Wi-Fi. If the network connection is moderate, the transfer unit can also perform power-saving data transfer using Bluetooth. This ensures the stability of data transfer by selecting the optimal transfer method according to the network connection status. Some or all of the above processing in the transfer unit may be performed using AI, for example, or without AI. For example, the transfer unit can input smartphone network connection status data into AI and have the AI ​​select the transfer method.

[0045] The transfer unit can select a data compression method during data transfer, taking into account the smartphone's storage capacity. For example, if the smartphone's storage capacity is low, the transfer unit will compress the data before transfer. The transfer unit can also transfer data without compression if the storage capacity is sufficient. If the storage capacity is moderate, the transfer unit can also transfer data with appropriate compression. This allows for efficient use of storage by selecting the optimal compression method according to the smartphone's storage capacity. Some or all of the above processing in the transfer unit may be performed using AI, for example, or without AI. For example, the transfer unit can input smartphone storage capacity data into AI and have the AI ​​select the compression method.

[0046] The transfer unit can adjust the transfer timing during data transfer, taking into account the usage status of the smartphone. For example, the transfer unit pauses data transfer when the smartphone is in use. The transfer unit adjusts the transfer timing during data transfer, taking into account the usage status of the smartphone. The transfer unit can also start data transfer when the smartphone is idle. The transfer unit can also prioritize data transfer when the smartphone is charging. This allows for adjusting the transfer timing according to the usage status of the smartphone, thereby improving user convenience. Some or all of the above-mentioned processing in the transfer unit may be performed using AI, for example, or may be performed without using AI. For example, the transfer unit can input smartphone usage status data into AI and have the AI ​​adjust the transfer timing.

[0047] The ordering unit can suggest optimal products by taking into account the user's past purchase history when placing an order. For example, the ordering unit suggests similar products based on products the user has previously purchased. The ordering unit can suggest optimal products by taking into account the user's past purchase history when placing an order. The ordering unit can also suggest new products that match the user's preferences based on the purchase history. The ordering unit can also analyze the purchase history and prioritize suggesting products that the user frequently purchases. This can improve user convenience by suggesting optimal products based on the user's past purchase history. Some or all of the above-described processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the user's past purchase history data into AI and have the AI ​​suggest optimal products.

[0048] The ordering unit can adjust the order quantity at the time of ordering, taking into account the user's current pace of ingredient consumption. For example, if the user's pace of ingredient consumption is fast, the ordering unit increases the order quantity. The ordering unit can adjust the order quantity at the time of ordering, taking into account the user's current pace of ingredient consumption. If the pace of ingredient consumption is slow, the ordering unit can also reduce the order quantity. The ordering unit can also analyze the pace of ingredient consumption and suggest the optimal order quantity. This makes it possible to order efficiently by adjusting the order quantity according to the user's pace of ingredient consumption. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can input the user's pace of ingredient consumption data into AI and have the AI ​​adjust the order quantity.

[0049] The order unit can select the optimal delivery method by taking into account the user's geographical location information when placing an order. The order unit, for example, selects the fastest delivery method based on the user's geographical location information. The order unit can also select a method that minimizes delivery costs by taking into account the geographical location information. The order unit can also select an environmentally friendly delivery method based on the geographical location information. This enables fast and efficient delivery by selecting the optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the order unit may be performed using, for example, AI, or may be performed without using AI. For example, the order unit can input the user's geographical location information data into AI and have the AI ​​select the delivery method.

[0050] The ordering unit can suggest the optimal payment option taking into account the user's payment method when placing an order. For example, the ordering unit can suggest similar payment options based on payment methods used by the user in the past. The ordering unit can suggest the optimal payment option taking into account the user's payment method when placing an order. The ordering unit can also present payment method options and suggest options that match the user's preferences. The ordering unit can also analyze the payment method and suggest the most convenient option. This can improve user convenience by suggesting the optimal payment option based on the user's payment method. Some or all of the above-mentioned processing in the ordering unit can be performed using, for example, AI, or can be performed without using AI. For example, the ordering unit can input the user's payment method data into AI and have the AI ​​suggest payment options.

[0051] When suggesting a recipe, the suggestion unit can suggest an optimal recipe by taking into account the user's past cooking history. For example, the suggestion unit may suggest similar recipes based on recipes created by the user in the past. When suggesting a recipe, the suggestion unit may suggest an optimal recipe by taking into account the user's past cooking history. The suggestion unit can also suggest new recipes that suit the user's preferences from the cooking history. The suggestion unit can also analyze the cooking history and prioritize suggesting recipes that the user frequently makes. This can improve user convenience by suggesting optimal recipes based on the user's past cooking history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past cooking history data into AI and have the AI ​​suggest optimal recipes.

[0052] When suggesting a recipe, the suggestion unit can customize the recipe taking into account the user's current ingredient inventory. For example, the suggestion unit suggests a recipe that makes effective use of inventory based on the current ingredient inventory. When suggesting a recipe, the suggestion unit customizes the recipe taking into account the user's current ingredient inventory. The suggestion unit can also suggest a recipe that takes into account ingredient inventory and supplements missing ingredients. The suggestion unit can also customize and suggest an optimal recipe by comprehensively considering ingredient inventory. This enables waste-free cooking by customizing a recipe based on the user's current ingredient inventory. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's current ingredient inventory data into AI and have the AI ​​customize the recipe.

[0053] When proposing a recipe, the suggestion unit can suggest an optimal recipe taking into account the user's ingredient allergy information. For example, the suggestion unit suggests recipes that do not contain allergens based on the user's allergy information. When proposing a recipe, the suggestion unit suggests an optimal recipe taking into account the user's ingredient allergy information. The suggestion unit can also suggest recipes that use alternative ingredients taking into account the allergy information. The suggestion unit can also suggest optimal recipes by comprehensively considering the allergy information. This makes it possible to cook allergy-friendly dishes by proposing optimal recipes based on the user's ingredient allergy information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's ingredient allergy information data into AI and have the AI ​​suggest optimal recipes.

[0054] The suggestion unit can customize a recipe taking into consideration the user's ingredient preferences when suggesting a recipe. The suggestion unit, for example, suggests a recipe that suits the user's preferences based on the user's ingredient preferences. The suggestion unit customizes a recipe taking into consideration the user's ingredient preferences when suggesting a recipe. The suggestion unit can also suggest recipes that use ingredients that the user likes, taking into consideration the ingredient preferences. The suggestion unit can also customize and suggest an optimal recipe by comprehensively considering the ingredient preferences. In this way, customizing a recipe based on the user's ingredient preferences makes it possible to cook a dish that suits the user's preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's ingredient preference data into AI and have the AI ​​customize the recipe.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The refrigerator management system can further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and provide information about ingredients in the refrigerator by voice. For example, if the user asks, "What's in the refrigerator?", the voice recognition unit can read out a list of ingredients in the refrigerator by voice based on data obtained from the analysis unit. Also, if the user asks, "How much milk is in stock?", the voice recognition unit can provide by voice the stock amount of a specific ingredient. Furthermore, if the user asks, "What recipes do you recommend?", recipe information obtained from the suggestion unit can be provided by voice. This allows the user to easily obtain information about the contents of the refrigerator through voice commands.

[0057] The camera installation unit can automatically optimize the placement of ingredients in the refrigerator. For example, the camera installation unit can suggest the optimal placement in the refrigerator based on the type and size of ingredients. The camera installation unit can also take into account the freshness of the ingredients and place fresher ingredients at the front. The camera installation unit can also place frequently used ingredients in a position that makes them easier to access based on how often they are used. In this way, optimizing the placement of ingredients in the refrigerator makes food management more efficient.

[0058] The analysis unit can learn the user's food consumption patterns and make consumption predictions. For example, the analysis unit can predict when a specific food ingredient will be consumed based on past consumption data. The analysis unit can also suggest when to replenish ingredients based on the consumption predictions. The analysis unit can also improve prediction accuracy by taking into account consumption patterns according to seasons and events. This makes it easier for users to understand when to consume ingredients and can reduce wasteful shopping.

[0059] When transferring data, the transfer unit can select a transfer method taking into account the location information of the user's smartphone. For example, when the user is at home, data can be transferred at high speed using Wi-Fi. When the user is out, the transfer unit can also transfer data using mobile data. Furthermore, when the user is in a specific location, the transfer unit can select the transfer method that is optimal for that location. This allows for improved data transfer efficiency by selecting the optimal transfer method according to the user's location information.

[0060] The ordering unit can learn the user's ingredient consumption patterns and suggest the optimal timing for ordering. For example, the ordering unit can predict when a particular ingredient will be consumed based on past consumption data and suggest ordering at the appropriate time. The ordering unit can also automatically adjust the timing for replenishing ingredients based on the consumption prediction. The ordering unit can also optimize the timing of ordering by taking into account consumption patterns according to seasons and events. This makes it easier for users to understand when to consume ingredients and reduces wasteful shopping.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The camera installation department installs a camera to visualize the food inside the refrigerator. The camera installation department installs the camera in a position that can cover the entire interior of the refrigerator. Step 2: The analysis unit analyzes the images acquired by the camera installation unit. The analysis unit uses AI to accurately determine the type and quantity of ingredients in stock. Step 3: The transfer unit transfers the data analyzed by the analysis unit to the smartphone. The transfer unit transfers the data in real time using Wi-Fi or Bluetooth. Step 4: The ordering unit manages the inventory of ingredients based on the data transferred by the transfer unit, and places an online order when the inventory falls below a threshold. The ordering unit sets a threshold for the inventory of ingredients, and places an online order when the inventory falls below the threshold. Step 5: The suggestion unit suggests specific recipes based on the data acquired by the analysis unit. The suggestion unit learns past recipe data and suggests appropriate recipes based on the current ingredients.

[0063] (Example 2) A system according to an embodiment of the present invention digitizes and efficiently manages ingredients stored in a refrigerator. This system installs a camera inside the refrigerator and visualizes the ingredients using camera images. The captured images are then transferred to a smartphone, where they are loaded and learned from previously created recipes. A threshold is set for the amount of ingredients in stock, and online orders are placed when the amount falls below the threshold. Furthermore, recommended recipes are suggested based on the captured images. For example, a camera is installed inside the refrigerator and visualizes the ingredients using camera images. The camera is positioned so that it can cover the entire interior of the refrigerator, and AI is used to accurately grasp the types of ingredients and the amount in stock. The captured images are then transferred to a smartphone. Wi-Fi and Bluetooth are used for real-time data transfer, allowing users to check the information about ingredients in the refrigerator on their smartphones. Previously created recipes are then loaded and the AI ​​learns from them. This allows optimal recipes to be suggested based on the current ingredients. A threshold is set for the amount of ingredients in stock, and online orders are placed when the amount falls below the threshold. This allows for automatic replenishment of ingredients. Finally, recommended recipes are suggested based on the captured images. AI is used to learn from past recipe data and suggest optimal recipes based on the current ingredients. This allows the system to efficiently manage ingredients in the refrigerator, automatically replenish stock, and suggest optimal recipes.

[0064] The refrigerator management system according to this embodiment comprises a camera installation unit, an analysis unit, a data transfer unit, an ordering unit, and a suggestion unit. The camera installation unit installs a camera to visualize the food items inside the refrigerator. The camera installation unit installs the camera in a position that can cover the entire interior of the refrigerator. The analysis unit analyzes the images acquired by the camera installation unit. The analysis unit accurately determines the types and quantities of food items using, for example, AI. The data transfer unit transfers the data analyzed by the analysis unit to a smartphone. The data transfer unit transfers the data in real time using, for example, Wi-Fi or Bluetooth. The ordering unit manages the food inventory based on the data transferred by the transfer unit and places an online order when the inventory falls below a threshold. The ordering unit sets a threshold for the food inventory and places an online order when the inventory falls below the threshold. The suggestion unit proposes specific recipes based on the data acquired by the analysis unit. The suggestion unit learns from past recipe data and proposes an appropriate recipe based on the current ingredients. As a result, the refrigerator management system according to this embodiment can efficiently manage the ingredients in the refrigerator, automatically replenish inventory when it runs low, and propose the optimal recipe.

[0065] The camera unit can be installed in a position that can cover the entire interior of the refrigerator. For example, the camera unit can be installed in a position that can cover the entire interior of the refrigerator. The camera unit can be installed in a position that can cover the entire interior of the refrigerator. This allows for accurate identification of food types and quantities by covering the entire interior of the refrigerator. Some or all of the above-described processes in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can cover the entire interior of the refrigerator by installing the camera in a position that can cover the entire interior of the refrigerator and adjusting the camera's field of view.

[0066] The analysis unit can accurately grasp the type and inventory of ingredients. The analysis unit, for example, uses AI to accurately grasp the type and inventory of ingredients. The analysis unit, for example, uses AI to accurately grasp the type and inventory of ingredients. The analysis unit, for example, uses AI to accurately grasp the type and inventory of ingredients. This allows for accurate understanding of the type and inventory of ingredients, enabling appropriate management. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input images acquired by the camera installation unit into AI, and have the AI ​​analyze the type and inventory of ingredients.

[0067] The transfer unit can transfer data instantly using Wi-Fi or Bluetooth. The transfer unit transfers data in real time using, for example, Wi-Fi or Bluetooth. The transfer unit transfers data instantly using Wi-Fi or Bluetooth. The transfer unit transfers data in real time using Wi-Fi or Bluetooth. By transferring data in real time, the user can instantly check information about ingredients in the refrigerator. Some or all of the above-described processing in the transfer unit may be performed using, for example, AI, or may be performed without using AI. For example, the transfer unit may input data analyzed by the analysis unit into AI and have the AI ​​transfer the data.

[0068] The ordering unit sets a threshold for the inventory of ingredients and can place an online order when the inventory falls below the threshold. The ordering unit, for example, sets a threshold for the inventory of ingredients and can place an online order when the inventory falls below the threshold. The ordering unit sets a threshold for the inventory of ingredients and can place an online order when the inventory falls below the threshold. The ordering unit sets a threshold for the inventory of ingredients and can place an online order when the inventory falls below the threshold. This allows ingredients to be automatically replenished when they are in short supply. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the data transferred by the transfer unit into AI and have the AI ​​manage the inventory of ingredients and place online orders.

[0069] The suggestion unit can learn past recipe data and suggest an appropriate recipe based on the current ingredients. The suggestion unit, for example, learns past recipe data and suggests an appropriate recipe based on the current ingredients. The suggestion unit learns past recipe data and suggests an appropriate recipe based on the current ingredients. The suggestion unit learns past recipe data and suggests an appropriate recipe based on the current ingredients. This makes it possible to suggest an optimal recipe based on the current ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data acquired by the analysis unit into AI and have the AI ​​execute recipe suggestions.

[0070] The camera installation unit can estimate the user's emotions and adjust the timing of camera capture based on the estimated user emotions. For example, if the user is feeling stressed, the camera installation unit can reduce the timing of camera capture to reduce the burden on the user. The camera installation unit can estimate the user's emotions and adjust the timing of camera capture based on the estimated user emotions. If the user is relaxed, the camera installation unit can also take frequent photos to obtain detailed data. If the user is in a hurry, the camera installation unit can also prioritize photographing only important ingredients. This can reduce the burden on the user by adjusting the timing of camera capture according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the camera installation unit may be performed using an AI, for example, or without an AI. For example, the camera installation unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of camera capture.

[0071] The camera installation unit can select an appropriate installation location taking into account the temperature and humidity inside the refrigerator. For example, the camera installation unit installs the camera in a location that avoids high temperatures inside the refrigerator to prevent fogging of the lens. The camera installation unit selects an appropriate installation location taking into account the temperature and humidity inside the refrigerator. The camera installation unit can also install the camera in a location that avoids high humidity inside the refrigerator to prevent camera failure. The camera installation unit can also select a location with a good balance between temperature and humidity to ensure the camera's long life. This allows the camera's performance to be maximized by optimizing the camera's installation location. Some or all of the above-mentioned processing in the camera installation unit may be performed using, or without, AI, for example. For example, the camera installation unit can input data on the temperature and humidity inside the refrigerator into AI and have the AI ​​select the optimal installation location.

[0072] The camera mounting unit can automatically recognize the arrangement of shelves and food items inside the refrigerator and determine the optimal mounting position. For example, the camera mounting unit can automatically recognize the arrangement of shelves and mount the camera in a position that does not obstruct the view. The camera mounting unit can automatically recognize the arrangement of shelves and food items inside the refrigerator and determine the optimal mounting position. The camera mounting unit can also automatically recognize the arrangement of food items and mount the camera in a position where important food items are visible. The camera mounting unit can also comprehensively consider the arrangement of shelves and food items and mount the camera in a position that ensures the optimal field of view. By optimizing the camera mounting position in this way, food items can be accurately identified without obstructing the view. Some or all of the above processing in the camera mounting unit may be performed using AI, for example, or without AI. For example, the camera mounting unit can input data on the arrangement of shelves and food items inside the refrigerator into AI and have the AI ​​determine the optimal mounting position.

[0073] The camera installation unit can estimate the user's emotions and adjust the camera's shooting angle based on the estimated user's emotions. For example, if the user is feeling stressed, the camera installation unit can capture a wide-angle image to grasp the entire scene at once. The camera installation unit can estimate the user's emotions and adjust the camera's shooting angle based on the estimated user's emotions. If the user is relaxed, the camera installation unit can also adjust the angle to capture details. If the user is in a hurry, the camera installation unit can also capture an image at an angle that focuses on important ingredients. This reduces the user's burden by adjusting the camera's shooting angle according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the camera installation unit can be performed using, for example, an AI. For example, the camera installation unit can input the user's emotion data into an AI and have the AI ​​adjust the camera's shooting angle.

[0074] The camera mounting unit can select an appropriate mounting position considering the frequency of opening and closing the refrigerator door. For example, the camera mounting unit can install the camera in a location that avoids frequent door opening and closing to prevent interference with filming. The camera mounting unit selects an appropriate mounting position considering the frequency of opening and closing the refrigerator door. The camera mounting unit can also select a location that is not frequently opened and closed to ensure a stable filming environment. The camera mounting unit can also install the camera in a fixed position to avoid vibrations caused by opening and closing the door. In this way, interference with filming can be prevented by optimizing the camera mounting position. Some or all of the above processing in the camera mounting unit may be performed using AI, for example, or without using AI. For example, the camera mounting unit can input data on the frequency of opening and closing the refrigerator door into the AI ​​and have the AI ​​select the optimal mounting position.

[0075] The camera installation unit can select an appropriate installation position taking into account the lighting conditions inside the refrigerator. For example, the camera installation unit installs the camera in a location that avoids direct lighting to prevent overexposure in the image. The camera installation unit selects an appropriate installation position taking into account the lighting conditions inside the refrigerator. The camera installation unit can also select a location where lighting is evenly shining and obtain bright, clear images. The camera installation unit can also avoid locations with poor lighting conditions and install the camera in a location where shadows are less likely to form. In this way, bright, clear images can be obtained by optimizing the camera installation position. Some or all of the above-described processing in the camera installation unit may be performed using, or without, AI. For example, the camera installation unit can input lighting condition data inside the refrigerator into AI and have the AI ​​select the optimal installation position.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple display method and reduces the amount of information. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. If the user is relaxed, the analysis unit can display detailed analysis results and provide a wealth of information. If the user is in a hurry, the analysis unit can highlight and display only important information. This reduces the burden on the user by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the display method of the analysis results.

[0077] The analysis unit can provide analysis results taking into account the freshness and expiration date of the ingredients. The analysis unit, for example, analyzes the freshness of the ingredients and displays them in order of freshness. The analysis unit provides analysis results taking into account the freshness and expiration date of the ingredients. The analysis unit can also prioritize displaying ingredients with an approaching expiration date to encourage early consumption. The analysis unit can also propose an optimal consumption order by comprehensively considering freshness and expiration date. In this way, the optimal consumption order can be proposed by taking into account the freshness and expiration date of the ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the freshness and expiration date of the ingredients into AI and have the AI ​​provide the analysis results.

[0078] The analysis unit can provide analysis results by adding nutritional value and calorie information of ingredients. For example, the analysis unit can analyze the nutritional value of ingredients and prioritize displaying ingredients with a good nutritional balance. The analysis unit can provide analysis results by adding nutritional value and calorie information of ingredients. The analysis unit can also analyze calorie information and prioritize displaying low-calorie ingredients. The analysis unit can also comprehensively consider nutritional value and calorie information and suggest healthy ingredients. In this way, healthy ingredients can be suggested by considering the nutritional value and calorie information of ingredients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input nutritional value and calorie information data of ingredients into AI and have the AI ​​perform the provision of analysis results.

[0079] The analysis unit can estimate the user's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying only important information. The analysis unit can also prioritize displaying detailed information if the user is relaxed. If the user is in a hurry, the analysis unit can also prioritize displaying information that can be quickly viewed. This reduces the user's burden by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​determine the priority of the analysis results.

[0080] The analysis unit can provide analysis results while taking into account the origin information of the ingredients. For example, the analysis unit can analyze the origin information of the ingredients and prioritize the display of locally produced ingredients. The analysis unit can provide analysis results while taking into account the origin information of the ingredients. The analysis unit can also distinguish and display imported ingredients and domestic ingredients based on the origin information. The analysis unit can also comprehensively consider the origin information and suggest ingredients that suit the user's preferences. In this way, by considering the origin information of the ingredients, it is possible to suggest ingredients that suit the user's preferences. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the origin information data of the ingredients into AI and have the AI ​​perform the provision of analysis results.

[0081] The analysis unit can provide analysis results by adding allergen information of food ingredients. For example, the analysis unit can analyze allergen information of food ingredients and display a warning for ingredients containing allergens. The analysis unit can also provide analysis results by adding allergen information of food ingredients. Based on the allergen information, the analysis unit can also prioritize displaying ingredients that do not contain allergens. The analysis unit can also comprehensively consider allergen information and suggest ingredients that are considerate of allergies. In this way, by considering allergen information of food ingredients, ingredients that are considerate of allergies can be suggested. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input allergen information data of food ingredients into AI and have the AI ​​perform the provision of analysis results.

[0082] The transfer unit can estimate the user's emotions and adjust the timing of data transfer based on the estimated user emotions. For example, if the user is feeling stressed, the transfer unit reduces the frequency of data transfers to reduce the user's burden. The transfer unit can estimate the user's emotions and adjust the timing of data transfer based on the estimated user emotions. If the user is relaxed, the transfer unit can also perform frequent data transfers and provide detailed information. If the user is in a hurry, the transfer unit can also prioritize the transfer of only important data. This reduces the user's burden by adjusting the timing of data transfer according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transfer unit may be performed using an AI, for example, or without an AI. For example, the transfer unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of data transfer.

[0083] The transfer unit can select a transfer method considering the smartphone's battery level during data transfer. For example, if the smartphone's battery level is low, the transfer unit will temporarily suspend data transfer. The transfer unit selects a transfer method considering the smartphone's battery level during data transfer. If the battery level is sufficient, the transfer unit can also perform high-speed data transfer using Wi-Fi. If the battery level is moderate, the transfer unit can also perform power-saving data transfer using Bluetooth. This allows for the selection of the optimal transfer method according to the smartphone's battery level, thereby reducing battery consumption. Some or all of the above processing in the transfer unit may be performed using AI, for example, or without AI. For example, the transfer unit can input smartphone battery level data into AI and have the AI ​​select the transfer method.

[0084] The transfer unit can select a transfer method considering the smartphone's network connection status during data transfer. For example, if the network connection is unstable, the transfer unit will temporarily suspend data transfer. The transfer unit selects a transfer method considering the smartphone's network connection status during data transfer. If the network connection is stable, the transfer unit can also perform high-speed data transfer using Wi-Fi. If the network connection is moderate, the transfer unit can also perform power-saving data transfer using Bluetooth. This ensures the stability of data transfer by selecting the optimal transfer method according to the network connection status. Some or all of the above processing in the transfer unit may be performed using AI, for example, or without AI. For example, the transfer unit can input smartphone network connection status data into AI and have the AI ​​select the transfer method.

[0085] The transfer unit can estimate the user's emotions and determine the priority of data transfer based on the estimated emotions. For example, if the user is stressed, the transfer unit will prioritize transferring only important data. The transfer unit can also prioritize transferring detailed data if the user is relaxed. If the user is in a hurry, the transfer unit can also prioritize transferring data that can be quickly viewed. This reduces the user's burden by prioritizing data transfer according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transfer unit may be performed using AI, for example, or not using AI. For example, the transfer unit can input user emotion data into an AI and have the AI ​​determine the priority of data transfer.

[0086] The transfer unit can select a data compression method during data transfer, taking into account the smartphone's storage capacity. For example, if the smartphone's storage capacity is low, the transfer unit will compress the data before transfer. The transfer unit can also transfer data without compression if the storage capacity is sufficient. If the storage capacity is moderate, the transfer unit can also transfer data with appropriate compression. This allows for efficient use of storage by selecting the optimal compression method according to the smartphone's storage capacity. Some or all of the above processing in the transfer unit may be performed using AI, for example, or without AI. For example, the transfer unit can input smartphone storage capacity data into AI and have the AI ​​select the compression method.

[0087] The transfer unit can adjust the transfer timing during data transfer, taking into account the usage status of the smartphone. For example, the transfer unit pauses data transfer when the smartphone is in use. The transfer unit adjusts the transfer timing during data transfer, taking into account the usage status of the smartphone. The transfer unit can also start data transfer when the smartphone is idle. The transfer unit can also prioritize data transfer when the smartphone is charging. This allows for adjusting the transfer timing according to the usage status of the smartphone, thereby improving user convenience. Some or all of the above-mentioned processing in the transfer unit may be performed using AI, for example, or may be performed without using AI. For example, the transfer unit can input smartphone usage status data into AI and have the AI ​​adjust the transfer timing.

[0088] The ordering unit can estimate the user's emotions and adjust the timing of orders based on the estimated user emotions. For example, if the user is feeling stressed, the ordering unit can reduce the frequency of orders and reduce the burden on the user. The ordering unit can estimate the user's emotions and adjust the timing of orders based on the estimated user emotions. If the user is relaxed, the ordering unit can also place orders frequently and provide detailed information. If the user is in a hurry, the ordering unit can also prioritize ordering only important ingredients. This reduces the burden on the user by adjusting the timing of orders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the ordering unit can be performed using an AI, for example, or without an AI. For example, the ordering unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of orders.

[0089] The ordering unit can suggest optimal products by taking into account the user's past purchase history when placing an order. For example, the ordering unit suggests similar products based on products the user has previously purchased. The ordering unit can suggest optimal products by taking into account the user's past purchase history when placing an order. The ordering unit can also suggest new products that match the user's preferences based on the purchase history. The ordering unit can also analyze the purchase history and prioritize suggesting products that the user frequently purchases. This can improve user convenience by suggesting optimal products based on the user's past purchase history. Some or all of the above-described processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the user's past purchase history data into AI and have the AI ​​suggest optimal products.

[0090] The ordering unit can adjust the order quantity at the time of ordering, taking into account the user's current pace of ingredient consumption. For example, if the user's pace of ingredient consumption is fast, the ordering unit increases the order quantity. The ordering unit can adjust the order quantity at the time of ordering, taking into account the user's current pace of ingredient consumption. If the pace of ingredient consumption is slow, the ordering unit can also reduce the order quantity. The ordering unit can also analyze the pace of ingredient consumption and suggest the optimal order quantity. This makes it possible to order efficiently by adjusting the order quantity according to the user's pace of ingredient consumption. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can input the user's pace of ingredient consumption data into AI and have the AI ​​adjust the order quantity.

[0091] The ordering system can estimate the user's emotions and prioritize orders based on those emotions. For example, if the user is stressed, the ordering system will prioritize ordering only essential ingredients. The ordering system can also prioritize detailed orders if the user is relaxed. If the user is in a hurry, the ordering system can prioritize ordering ingredients that can be quickly checked. This reduces the user's burden by prioritizing orders according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using AI or not. For example, the ordering system can input user emotion data into an AI and have the AI ​​determine the order priorities.

[0092] The order unit can select the optimal delivery method by taking into account the user's geographical location information when placing an order. The order unit, for example, selects the fastest delivery method based on the user's geographical location information. The order unit can also select a method that minimizes delivery costs by taking into account the geographical location information. The order unit can also select an environmentally friendly delivery method based on the geographical location information. This enables fast and efficient delivery by selecting the optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the order unit may be performed using, for example, AI, or may be performed without using AI. For example, the order unit can input the user's geographical location information data into AI and have the AI ​​select the delivery method.

[0093] The ordering unit can suggest the optimal payment option taking into account the user's payment method when placing an order. For example, the ordering unit can suggest similar payment options based on payment methods used by the user in the past. The ordering unit can suggest the optimal payment option taking into account the user's payment method when placing an order. The ordering unit can also present payment method options and suggest options that match the user's preferences. The ordering unit can also analyze the payment method and suggest the most convenient option. This can improve user convenience by suggesting the optimal payment option based on the user's payment method. Some or all of the above-mentioned processing in the ordering unit can be performed using, for example, AI, or can be performed without using AI. For example, the ordering unit can input the user's payment method data into AI and have the AI ​​suggest payment options.

[0094] The suggestion unit can estimate the user's emotions and adjust the way in which recipe suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest simple recipes and reduce the amount of information. The suggestion unit can estimate the user's emotions and adjust the way in which recipe suggestions are presented based on the estimated user emotions. If the user is relaxed, the suggestion unit can also suggest detailed recipes and provide a wealth of information. If the user is in a hurry, the suggestion unit can also suggest recipes by emphasizing only the important points. This reduces the burden on the user by adjusting the way in which recipe suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into an AI and have the AI ​​adjust the way in which recipe suggestions are presented.

[0095] When suggesting a recipe, the suggestion unit can suggest an optimal recipe by taking into account the user's past cooking history. For example, the suggestion unit may suggest similar recipes based on recipes created by the user in the past. When suggesting a recipe, the suggestion unit may suggest an optimal recipe by taking into account the user's past cooking history. The suggestion unit can also suggest new recipes that suit the user's preferences from the cooking history. The suggestion unit can also analyze the cooking history and prioritize suggesting recipes that the user frequently makes. This can improve user convenience by suggesting optimal recipes based on the user's past cooking history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past cooking history data into AI and have the AI ​​suggest optimal recipes.

[0096] When suggesting a recipe, the suggestion unit can customize the recipe taking into account the user's current ingredient inventory. For example, the suggestion unit suggests a recipe that makes effective use of inventory based on the current ingredient inventory. When suggesting a recipe, the suggestion unit customizes the recipe taking into account the user's current ingredient inventory. The suggestion unit can also suggest a recipe that takes into account ingredient inventory and supplements missing ingredients. The suggestion unit can also customize and suggest an optimal recipe by comprehensively considering ingredient inventory. This enables waste-free cooking by customizing a recipe based on the user's current ingredient inventory. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's current ingredient inventory data into AI and have the AI ​​customize the recipe.

[0097] The suggestion unit can estimate the user's emotions and prioritize recipe suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit prioritizes simple and easy recipes. The suggestion unit can estimate the user's emotions and prioritize recipe suggestions based on the estimated user emotions. If the user is relaxed, the suggestion unit can also prioritize detailed recipes. If the user is in a hurry, the suggestion unit can also prioritize recipes that can be made quickly. This reduces the burden on the user by prioritizing recipe suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into an AI and have the AI ​​determine the priority of recipe suggestions.

[0098] When proposing a recipe, the suggestion unit can suggest an optimal recipe taking into account the user's ingredient allergy information. For example, the suggestion unit suggests recipes that do not contain allergens based on the user's allergy information. When proposing a recipe, the suggestion unit suggests an optimal recipe taking into account the user's ingredient allergy information. The suggestion unit can also suggest recipes that use alternative ingredients taking into account the allergy information. The suggestion unit can also suggest optimal recipes by comprehensively considering the allergy information. This makes it possible to cook allergy-friendly dishes by proposing optimal recipes based on the user's ingredient allergy information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's ingredient allergy information data into AI and have the AI ​​suggest optimal recipes.

[0099] The suggestion unit can customize a recipe taking into consideration the user's ingredient preferences when suggesting a recipe. The suggestion unit, for example, suggests a recipe that suits the user's preferences based on the user's ingredient preferences. The suggestion unit customizes a recipe taking into consideration the user's ingredient preferences when suggesting a recipe. The suggestion unit can also suggest recipes that use ingredients that the user likes, taking into consideration the ingredient preferences. The suggestion unit can also customize and suggest an optimal recipe by comprehensively considering the ingredient preferences. In this way, customizing a recipe based on the user's ingredient preferences makes it possible to cook a dish that suits the user's preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's ingredient preference data into AI and have the AI ​​customize the recipe. === Hard Collateral 1-1 === Each of the multiple elements described above, including the camera installation unit, analysis unit, transfer unit, ordering unit, and suggestion unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the camera installation unit is implemented by the camera 42 of the smart device 14 and visualizes the food items inside the refrigerator. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and accurately determines the type and quantity of food items using AI. The transfer unit transfers data via Wi-Fi or Bluetooth using, for example, the communication I / F 44 of the smart device 14. The ordering unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and sets a threshold for the quantity of food items in stock, and places an online order when the quantity falls below the threshold. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and learns past recipe data to suggest the optimal recipe based on the current ingredients. === Hard Collateral 1-2 === Each of the multiple elements, including the camera installation unit, analysis unit, transfer unit, ordering unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the camera installation unit is realized by the camera 42 of the smart glasses 214 and visualizes ingredients in the refrigerator. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accurately determines the type and inventory of ingredients using AI. The transfer unit transfers data via Wi-Fi or Bluetooth using the communication I / F 44 of the smart glasses 214. The ordering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets a threshold for the inventory of ingredients and places an online order when the inventory falls below the threshold. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns past recipe data and suggests optimal recipes based on current ingredients. === Hard Collateral 1-3 === Each of the multiple elements, including the camera installation unit, analysis unit, transfer unit, ordering unit, and suggestion unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the camera installation unit is realized by the camera 42 of the headset terminal 314 and visualizes ingredients in the refrigerator. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accurately determines the type and inventory of ingredients using AI. The transfer unit transfers data via Wi-Fi or Bluetooth using the communication I / F 44 of the headset terminal 314. The ordering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets a threshold for the inventory of ingredients and places an online order when the inventory falls below the threshold. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns past recipe data and suggests optimal recipes based on current ingredients. === Hard Collateral 1-4 === Each of the multiple elements described above, including the camera installation unit, analysis unit, transfer unit, ordering unit, and suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the camera installation unit is implemented by the camera 42 of the robot 414, which visualizes the food items inside the refrigerator. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to accurately determine the type and quantity of food items in stock. The transfer unit transfers data via Wi-Fi or Bluetooth using, for example, the communication I / F 44 of the robot 414. The ordering unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which sets a threshold for the quantity of food items in stock and places an online order when the quantity falls below the threshold. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which learns past recipe data and suggests the optimal recipe based on the current ingredients.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The refrigerator management system can further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and provide information about ingredients in the refrigerator by voice. For example, if the user asks, "What's in the refrigerator?", the voice recognition unit can read out a list of ingredients in the refrigerator by voice based on data obtained from the analysis unit. Also, if the user asks, "How much milk is in stock?", the voice recognition unit can provide by voice the stock amount of a specific ingredient. Furthermore, if the user asks, "What recipes do you recommend?", recipe information obtained from the suggestion unit can be provided by voice. This allows the user to easily obtain information about the contents of the refrigerator through voice commands.

[0102] The camera installation unit can automatically optimize the placement of ingredients in the refrigerator. For example, the camera installation unit can suggest the optimal placement in the refrigerator based on the type and size of ingredients. The camera installation unit can also take into account the freshness of the ingredients and place fresher ingredients at the front. The camera installation unit can also place frequently used ingredients in a position that makes them easier to access based on how often they are used. In this way, optimizing the placement of ingredients in the refrigerator makes food management more efficient.

[0103] The analysis unit can learn the user's food consumption patterns and make consumption predictions. For example, the analysis unit can predict when a specific food ingredient will be consumed based on past consumption data. The analysis unit can also suggest when to replenish ingredients based on the consumption predictions. The analysis unit can also improve prediction accuracy by taking into account consumption patterns according to seasons and events. This makes it easier for users to understand when to consume ingredients and can reduce wasteful shopping.

[0104] When transferring data, the transfer unit can select a transfer method taking into account the location information of the user's smartphone. For example, when the user is at home, data can be transferred at high speed using Wi-Fi. When the user is out, the transfer unit can also transfer data using mobile data. Furthermore, when the user is in a specific location, the transfer unit can select the transfer method that is optimal for that location. This allows for improved data transfer efficiency by selecting the optimal transfer method according to the user's location information.

[0105] The ordering unit can learn the user's ingredient consumption patterns and suggest the optimal timing for ordering. For example, the ordering unit can predict when a particular ingredient will be consumed based on past consumption data and suggest ordering at the appropriate time. The ordering unit can also automatically adjust the timing for replenishing ingredients based on the consumption prediction. The ordering unit can also optimize the timing of ordering by taking into account consumption patterns according to seasons and events. This makes it easier for users to understand when to consume ingredients and reduces wasteful shopping.

[0106] The suggestion unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a simple and easy recipe. If the user is relaxed, the suggestion unit can also suggest a challenging recipe. Also, if the user is in a hurry, the suggestion unit can suggest a recipe that can be made in a short time. In this way, the burden of cooking can be reduced by adjusting the difficulty of the recipe according to the user's emotions.

[0107] The camera installation unit can estimate the user's emotions and adjust the camera resolution based on the estimated user emotions. For example, if the user is feeling stressed, it can take pictures at low resolution to reduce the amount of data. If the user is relaxed, the camera installation unit can take pictures at high resolution to obtain detailed data. Also, if the user is in a hurry, it can take pictures of only important ingredients at high resolution. In this way, the amount of data can be optimized by adjusting the camera resolution according to the user's emotions.

[0108] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a simple notification method can be provided, reducing the amount of information. If the user is relaxed, the analysis unit can also notify the user of detailed analysis results. Also, if the user is in a hurry, the analysis unit can notify the user by emphasizing only important information. In this way, the burden on the user can be reduced by adjusting the notification method of the analysis results according to the user's emotions.

[0109] The transfer unit can estimate the user's emotions and adjust the frequency of data transfers based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data transfers can be reduced to reduce the user's burden. If the user is relaxed, the transfer unit can transfer data frequently to provide detailed information. Also, if the user is in a hurry, the transfer unit can transfer only important data preferentially. In this way, the user's burden can be reduced by adjusting the frequency of data transfers according to the user's emotions.

[0110] The ordering unit can estimate the user's emotions and determine the order priority based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize ordering only important ingredients. If the user is relaxed, the ordering unit can also prioritize detailed orders. Also, if the user is in a hurry, it can prioritize ordering ingredients that can be checked quickly. In this way, the burden on the user can be reduced by determining the order priority according to the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The camera installation department installs a camera to visualize the food inside the refrigerator. The camera installation department installs the camera in a position that can cover the entire interior of the refrigerator. Step 2: The analysis unit analyzes the images acquired by the camera installation unit. The analysis unit uses AI to accurately determine the type and quantity of ingredients in stock. Step 3: The transfer unit transfers the data analyzed by the analysis unit to the smartphone. The transfer unit transfers the data in real time using Wi-Fi or Bluetooth. Step 4: The ordering unit manages the inventory of ingredients based on the data transferred by the transfer unit, and places an online order when the inventory falls below a threshold. The ordering unit sets a threshold for the inventory of ingredients, and places an online order when the inventory falls below the threshold. Step 5: The suggestion unit suggests specific recipes based on the data acquired by the analysis unit. The suggestion unit learns past recipe data and suggests appropriate recipes based on the current ingredients.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a camera installation unit for visualizing ingredients in the refrigerator; an analysis unit that analyzes the image acquired by the camera installation unit; A transfer unit that transfers the data analyzed by the analysis unit to a smartphone; an ordering unit that manages the inventory of ingredients based on the data transferred by the transfer unit and places an online order when the inventory falls below a threshold; a suggestion unit that suggests a specific recipe based on the data acquired by the analysis unit. A system characterized by:

2. The camera installation unit includes: It is installed in a position that can cover the entire interior of the refrigerator.

2. The system of claim 1.

3. The analysis unit Accurately understand the types of ingredients and stock levels 2. The system of claim 1.

4. The transfer unit Instant data transfer via Wi-Fi or Bluetooth 2. The system of claim 1.

5. The ordering unit Set a threshold for the amount of ingredients in stock, and place an online order when the amount falls below that threshold.

2. The system of claim 1.

6. The proposal unit Learns from past recipe data and suggests appropriate recipes based on current ingredients 2. The system of claim 1.

7. The camera installation unit includes: The system estimates the user's emotions and adjusts the timing of camera capture based on the estimated user emotions.

2. The system of claim 1.

8. The camera installation unit includes: Select an appropriate installation location taking into consideration the temperature and humidity inside the refrigerator.

2. The system of claim 1.

9. The camera installation unit includes: Automatically recognizes the layout of shelves and ingredients inside the refrigerator and determines the optimal installation position 2. The system of claim 1.

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